Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification

at different bacteria-nanoparticle coincubation time points, we constructed joint SERS spectra for predictive analytics using machine learning (ML) algorithms. We have successfully achieved the precise classification of various pollutants with high prediction accuracy, including different types and forms of heavy metals (100%) and different PFASs (≥92%), as well as the quantification of representative pollutants. The successful detection of different heavy metal ions and PFASs in seawater demonstrates its potential for detecting and distinguishing harmful pollutants in complex real-world environments. This work demonstrates a facile and efficient WCB platform for pollutant classification and quantification, providing an effective analytical method for environmental monitoring.